ANMM4CBR: a case-based reasoning method for gene expression data classification

作者：Yao, Bangpeng; Li, Shao^*

来源：Algorithms for Molecular Biology, 2010, 5(1): 14.

DOI：10.1186/1748-7188-5-14

• NONPARAMETRIC DISCRIMINANT-ANALYSIS
• MICROARRAY DATA
• CANCER
• DIAGNOSIS
• TUMOR
• PREDICTION
• DISCOVERY
• PATTERNS

Background: Accurate classification of microarray data is critical for successful clinical diagnosis and treatment. The "curse of dimensionality" problem and noise in the data, however, undermines the performance of many algorithms. @@@ Method: In order to obtain a robust classifier, a novel Additive Nonparametric Margin Maximum for Case-Based Reasoning (ANMM4CBR) method is proposed in this article. ANMM4CBR employs a case-based reasoning (CBR) method for classification. CBR is a suitable paradigm for microarray analysis, where the rules that define the domain knowledge are difficult to obtain because usually only a small number of training samples are available. Moreover, in order to select the most informative genes, we propose to perform feature selection via additively optimizing a nonparametric margin maximum criterion, which is defined based on gene pre-selection and sample clustering. Our feature selection method is very robust to noise in the data. @@@ Results: The effectiveness of our method is demonstrated on both simulated and real data sets. We show that the ANMM4CBR method performs better than some state-of-the-art methods such as support vector machine (SVM) and k nearest neighbor (kNN), especially when the data contains a high level of noise. @@@ Availability: The source code is attached as an additional file of this paper.

• 出版日期2010-1-6
• 单位清华大学